[Core] Dynamic image size support for VLMs

docs/source/dev/input_processing/model_inputs_index.rst

@@ -8,7 +8,7 @@ Input Processing
 vLLM provides a mechanism for defining input processors for each model so that the inputs are processed
 in :class:`~vllm.LLMEngine` before they are passed to model executors. 
 
-Currently, this mechanism is only utilized in **multi-modal models** for preprocessing multi-modal input 
+Currently, this mechanism is only utilized in :ref:`multi-modal models <multi_modality>` for preprocessing multi-modal input 
 data in addition to input prompt, but it can be extended to text-only language models when needed.
 
 Guides

docs/source/dev/multimodal/adding_multimodal_model.rst

@@ -0,0 +1,124 @@
+.. _adding_a_new_multimodal_model:
+
+Adding a New Multimodal Model
+=============================
+
+This document provides a high-level guide on integrating a :ref:`multi-modal model <multi_modality>` into vLLM.
+
+.. note::
+    The complexity of adding a new model depends heavily on the model's architecture.
+    The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
+    However, for models that include new operators (e.g., a new attention mechanism), the process can be a bit more complex.
+
+.. tip::
+    If you are encountering issues while integrating your model into vLLM, feel free to open an issue on our `GitHub <https://github.com/vllm-project/vllm/issues>`_ repository.
+    We will be happy to help you out!
+
+
+1. Set up the base vLLM model
+-----------------------------
+
+As usual, follow :ref:`these steps <adding_a_new_model>` to implement the model in vLLM, but note the following:
+
+- You should additionally implement the :class:`~vllm.model_executor.models.interfaces.SupportsVision` interface.
+
+  .. code-block:: diff
+
+      + from vllm.model_executor.models.interfaces import SupportsVision
+
+      - class YourModelForImage2Seq(nn.Module):
+      + class YourModelForImage2Seq(nn.Module, SupportsVision):
+
+  .. note::
+      The model class does not have to be named :code:`*ForCausalLM`.
+      Check out `the HuggingFace Transformers documentation <https://huggingface.co/docs/transformers/model_doc/auto#multimodal>`__ for some examples.
+
+- While implementing the :meth:`~torch.nn.Module.forward` method, reserve a keyword parameter
+  for each input tensor that corresponds to a multi-modal input, as shown in the following example:
+
+  .. code-block:: diff
+
+      def forward(
+          self,
+          input_ids: torch.Tensor,
+          positions: torch.Tensor,
+          kv_caches: List[torch.Tensor],
+          attn_metadata: AttentionMetadata,
+      +   pixel_values: torch.Tensor,
+      ) -> SamplerOutput:
+
+
+2. Register input mappers
+-------------------------
+
+For each modality type to support, decorate the model class with :meth:`MULTIMODAL_REGISTRY.register_input_mapper <vllm.multimodal.MultiModalRegistry.register_input_mapper>`.
+This decorator accepts a function that maps multi-modal inputs to the keyword arguments you have previously defined in :meth:`~torch.nn.Module.forward`.
+
+.. code-block:: diff
+
+    from vllm.model_executor.models.interfaces import SupportsVision
+    + from vllm.multimodal import MULTIMODAL_REGISTRY
+
+    + @MULTIMODAL_REGISTRY.register_image_feature_input_mapper()
+    + @MULTIMODAL_REGISTRY.register_image_pixel_input_mapper()
+    class YourModelForImage2Seq(nn.Module, SupportsVision):
+
+A default mapper is available for each modality in the core vLLM library. This input mapper will be used if you do not provide your own function.
+
+.. seealso::
+    :ref:`input_processing_pipeline`
+
+
+3. (Optional) Register dummy data
+---------------------------------
+
+During startup, dummy data is passed to the vLLM model to allocate memory. This only consists of text input by default, which may not be applicable to multi-modal models.
+In such cases, you can define your own dummy data by registering a factory method via :meth:`INPUT_REGISTRY.register_dummy_data <vllm.inputs.registry.InputRegistry.register_dummy_data>`.
+
+.. code-block:: diff
+
+    from vllm.inputs import INPUT_REGISTRY
+    from vllm.model_executor.models.interfaces import SupportsVision
+    from vllm.multimodal import MULTIMODAL_REGISTRY
+
+    @MULTIMODAL_REGISTRY.register_image_feature_input_mapper()
+    @MULTIMODAL_REGISTRY.register_image_pixel_input_mapper()
+    + @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>)
+    class YourModelForImage2Seq(nn.Module, SupportsVision):
+
+Here are some examples:
+
+- Image inputs (static feature size): `LLaVA-1.5 Model <https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava.py>`__
+- Image inputs (dynamic feature size): `LLaVA-NeXT Model <https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava_next.py>`__
+
+.. seealso::
+    :ref:`input_processing_pipeline`
+
+
+4. (Optional) Register input processor
+--------------------------------------
+
+Sometimes, there is a need to process inputs at the :class:`~vllm.LLMEngine` level before they are passed to the model executor. 
+This is often due to the fact that unlike implementations in HuggingFace Transformers, the reshaping and/or expansion of multi-modal embeddings needs to take place outside model's :meth:`~torch.nn.Module.forward` call.
+You can register input processors via :meth:`INPUT_REGISTRY.register_input_processor <vllm.inputs.registry.InputRegistry.register_input_processor>`.
+
+.. code-block:: diff
+
+    from vllm.inputs import INPUT_REGISTRY
+    from vllm.model_executor.models.interfaces import SupportsVision
+    from vllm.multimodal import MULTIMODAL_REGISTRY
+
+    @MULTIMODAL_REGISTRY.register_image_feature_input_mapper()
+    @MULTIMODAL_REGISTRY.register_image_pixel_input_mapper()
+    @INPUT_REGISTRY.register_dummy_data(<your_dummy_data_factory>)
+    + @INPUT_REGISTRY.register_input_processor(<your_input_processor>)
+    class YourModelForImage2Seq(nn.Module, SupportsVision):
+
+A common use case of input processors is inserting placeholder tokens to leverage the vLLM framework for attention mask generation.
+Here are some examples:
+
+- Insert static number of image tokens: `LLaVA-1.5 Model <https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava.py>`__
+- Insert dynamic number of image tokens: `LLaVA-NeXT Model <https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava_next.py>`__
+
+.. seealso::
+    :ref:`input_processing_pipeline`

docs/source/dev/multimodal/multimodal_index.rst

@@ -1,3 +1,5 @@
+.. _multi_modality:
+
 Multi-Modality
 ==============
 
@@ -8,12 +10,18 @@ vLLM provides experimental support for multi-modal models through the :mod:`vllm
 :class:`vllm.inputs.PromptStrictInputs` accepts an additional attribute ``multi_modal_data``
 which allows you to pass in multi-modal input alongside text and token prompts.
 
-By default, vLLM models do not support multi-modal inputs. To enable multi-modal support for a model,
-you must decorate the model class with :meth:`InputRegistry.register_dummy_data <vllm.inputs.registry.InputRegistry.register_dummy_data>`,
-as well as :meth:`MULTIMODAL_REGISTRY.register_input_mapper <MultiModalRegistry.register_input_mapper>` for each modality type to support.
+By default, vLLM models do not support multi-modal inputs. To enable multi-modal support for a model, please follow :ref:`the guide for adding a new multimodal model. <adding_a_new_multimodal_model>`.
 
 # TODO: Add more instructions on how to do that once embeddings is in.
 
+Guides
+++++++
+
+.. toctree::
+   :maxdepth: 1
+
+   adding_multimodal_model
+
 Module Contents
 +++++++++++++++
 
@@ -35,6 +43,10 @@ Base Classes
     :members:
     :show-inheritance:
 
+.. autoclass:: vllm.multimodal.MultiModalInputs
+    :members:
+    :show-inheritance:
+
 .. autoclass:: vllm.multimodal.MultiModalPlugin
     :members:
     :show-inheritance:

docs/source/models/vlm.rst

@@ -23,7 +23,6 @@ The following :ref:`engine arguments <engine_args>` are specific to VLMs:
     Currently, the support for vision language models on vLLM has the following limitations:
 
     * Only single image input is supported per text prompt.
-    * Dynamic ``image_input_shape`` is not supported: the input image will be resized to the static ``image_input_shape``. This means our LLaVA-NeXT output may not exactly match the huggingface implementation.
 
     We are continuously improving user & developer experience for VLMs. Please `open an issue on GitHub <https://github.com/vllm-project/vllm/issues/new/choose>`_ if you have any feedback or feature requests.
 
@@ -42,12 +41,17 @@ To initialize a VLM, the aforementioned arguments must be passed to the ``LLM``
     )
 
 .. important::
+    Currently, you have to specify ``image_feature_size`` to support memory profiling.
+    To avoid OOM during runtime, you should set this to the maximum value supported by the model.
+    The calculation of feature size is specific to the model. For more details, please refer to
+    the function :code:`get_<model_name>_image_feature_size` inside the corresponding model file.
+
     We will remove most of the vision-specific arguments in a future release as they can be inferred from the HuggingFace configuration.
 
 
 To pass an image to the model, note the following in :class:`vllm.inputs.PromptStrictInputs`:
 
-* ``prompt``: The prompt should have a number of ``<image>`` tokens equal to ``image_feature_size``.
+* ``prompt``: The prompt should follow the format that is documented on HuggingFace.
 * ``multi_modal_data``: This is a dictionary that follows the schema defined in :class:`vllm.multimodal.MultiModalDataDict`. 
 
 .. note::
@@ -57,8 +61,8 @@ To pass an image to the model, note the following in :class:`vllm.inputs.PromptS
 
 .. code-block:: python
 
-    prompt = "<image>" * 576 + (
-        "\nUSER: What is the content of this image?\nASSISTANT:")
+    # Refer to the HuggingFace repo for the correct format to use
+    prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
 
     # Load the image using PIL.Image
     image = ...
@@ -74,8 +78,6 @@ To pass an image to the model, note the following in :class:`vllm.inputs.PromptS
 
 A code example can be found in `examples/llava_example.py <https://github.com/vllm-project/vllm/blob/main/examples/llava_example.py>`_.
 
-.. important::
-    We will remove the need to format image tokens in a future release. Afterwards, the input text will follow the same format as that for the original HuggingFace model.
 
 Online OpenAI Vision API Compatible Inference
 ----------------------------------------------
@@ -103,6 +105,11 @@ Below is an example on how to launch the same ``llava-hf/llava-1.5-7b-hf`` with
         --chat-template template_llava.jinja
 
 .. important::
+    Currently, you have to specify ``image_feature_size`` to support memory profiling.
+    To avoid OOM during runtime, you should set this to the maximum value supported by the model.
+    The calculation of feature size is specific to the model. For more details, please refer to
+    the function :code:`get_<model_name>_image_feature_size` inside the corresponding model file.
+
     We will remove most of the vision-specific arguments in a future release as they can be inferred from the HuggingFace configuration.
 
 To consume the server, you can use the OpenAI client like in the example below:
@@ -121,6 +128,8 @@ To consume the server, you can use the OpenAI client like in the example below:
         messages=[{
             "role": "user",
             "content": [
+                # NOTE: The prompt formatting with the image token `<image>` is not needed
+                # since the prompt will be processed automatically by the API server.
                 {"type": "text", "text": "What's in this image?"},
                 {
                     "type": "image_url",
@@ -144,5 +153,4 @@ A full code example can be found in `examples/openai_vision_api_client.py <https
         export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
 
 .. note::
-    The prompt formatting with the image token ``<image>`` is not needed when serving VLMs with the API server since the prompt will be 
-    processed automatically by the server.
+    There is no need to format the prompt in the API request since it will be handled by the server.

examples/llava_example.py

@@ -17,8 +17,7 @@ def run_llava():
         image_feature_size=576,
     )
 
-    prompt = "<image>" * 576 + (
-        "\nUSER: What is the content of this image?\nASSISTANT:")
+    prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:"
 
     image = Image.open("images/stop_sign.jpg")
 

examples/llava_next_example.py

@@ -5,22 +5,17 @@
 
 from vllm import LLM, SamplingParams
 
-# Dynamic image input is currently not supported and therefore
-# a fixed image input shape and its corresponding feature size is required.
-# See https://github.com/vllm-project/vllm/pull/4199 for the complete
-# configuration matrix.
-
 
 def run_llava_next():
     llm = LLM(
         model="llava-hf/llava-v1.6-mistral-7b-hf",
         image_token_id=32000,
         image_input_shape="1,3,336,336",
-        image_feature_size=1176,
+        # Use the maximum possible value for memory profiling
+        image_feature_size=2928,
     )
 
-    prompt = "[INST] " + "<image>" * 1176 + (
-        "\nWhat is shown in this image? [/INST]")
+    prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
     url = "https://h2o-release.s3.amazonaws.com/h2ogpt/bigben.jpg"
     image = Image.open(BytesIO(requests.get(url).content))
     sampling_params = SamplingParams(temperature=0.8,

examples/phi3v_example.py

@@ -5,6 +5,9 @@
 
 from vllm import LLM, SamplingParams
 
+# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
+# You can use `.buildkite/download-images.sh` to download them
+
 
 def run_phi3v():
     model_path = "microsoft/Phi-3-vision-128k-instruct"
@@ -18,16 +21,15 @@ def run_phi3v():
         trust_remote_code=True,
         image_token_id=32044,
         image_input_shape="1,3,1008,1344",
-        image_feature_size=1921,
+        # Use the maximum possible value for memory profiling
+        image_feature_size=2653,
         max_num_seqs=5,
     )
 
     image = Image.open("images/cherry_blossom.jpg")
 
     # single-image prompt
     prompt = "<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n"  # noqa: E501
-    prompt = prompt.replace("<|image_1|>", "<|image|>" * 1921 + "<s>")
-
     sampling_params = SamplingParams(temperature=0, max_tokens=64)
 
     outputs = llm.generate(